The task of finding relevant data among vast numbers of documents, articles, websites, and other content can be daunting. Databases that contain large numbers of entries are often time-consuming and costly to query. Further, in complex systems, databases may be continually updated with new data, and existing documents, articles, websites, or other content may be modified or deleted. A simple query on a large database could take several hours, if not days, to perform.
The task of searching for the most recent clusters of related data (e.g., websites or news articles pertaining to the same professional football team) presents additional challenges. For example, if the search is conditioned on both the name of the football team and a date or time range, the search will require more processing, and hence more time, more hardware usage, and more cost.
One solution, albeit flawed, to the challenge of searching for the most recent data of a given type is to pre-compute the search and store the results in a database or cache. In this approach, a subsequent search may quickly access the stored results without having to run a new search.
But the drawbacks of this purported solution are significant. For one thing, the solution is ineffective when the data being queried is regularly updated, because the stored search results will not be able to account for newly added, modified, or deleted records. Another problem is that different querying applications (e.g., web search engines, social media search functionality, online “fan page” search functionality, etc.) may have different needs in querying the data, and therefore pre-computed search results may be relevant to one application's needs but less relevant to another application's needs.
Furthermore, the filtering properties of any given application may change over time. For example, an application may need to query the data with filters based on date, keywords, author, URL, peer reviews, etc. Pre-computed search results would again fail to deliver relevant results. Moreover, if the cache in which pre-computed data is stored experienced a software or hardware failure, the pre-computed search results would no longer be accessible, and a high volume of incoming queries would quickly overwhelm the query system.